Perception is an essential component of intelligent behavior. We perceive the world around us through five basic senses of sight, hearing , touch, smell, and taste., of these, sight and hearing have been the main areas of Artificial Intelligence research leading to speech understanding . when we perceive some signal . it may a be sound or light. We respond appropriately to that signal. To produce an appropriate response we must categorize or analyze that signal. For example to analyze a sentence we must first identify individual sounds, then combine these sounds into words, and then combine words into a meaningful sentence structure . but this is hard because dividing sounds into words needs additional knowledge or information about the situation. A series of sounds may be interpreted in many ways . For instance
“Tigers care their kids”
and “Tiger scare their kids”
might both have been the possible interpretations of the same series of sounds.
To overcome the perceptual problems in speech understanding , the process of analyzing a speech is divided into five stages.
1. Digitization : The continuous input is divided into discrete chunks . in speech the division is done on a time scale and in images, it may be based on
color or area or tint.
2. Smoothing: Since the real world is usually continuous , large spikes and variation in the input is avoided.
3. Segmentation: Group the smaller chunks produced by digitization into larger chunks corresponding to logic components of the signal. For speech understanding segments correspond to individual sounds called phonemes.
4. Labeling: Each segment is given a label.
5. Analysis : The labeled segments are put together to form a coherent object.
Learning is the improvement of performance with experience over time.
Learning element is the portion of a learning AI system that decides how to modify the performance element and implements those modifications.
1. Memorization (rote learning)
2. Direct instruction (by being told)
Learning by memorizations is the simplest from of le4arning. It requires the least amount of inference and is accomplished by simply copying the knowledge in the same form that it will be used directly into the knowledge base.
Example:- Memorizing multiplication tables, formulate , etc.
Direct instruction is a complex form of learning. This type of learning requires more inference than role learning since the knowledge must be transformed into an operational form before learning when a teacher presents a number of facts directly to us in a well organized manner.
Analogical learning is the process of learning a new concept or solution through the use of similar known concepts or solutions. We use this type of learning when solving problems on an exam where previously learned examples serve as a guide or when make frequent use of analogical learning. This form of learning requires still more inferring than either of the previous forms. Since difficult transformations must be made between the known and unknown situations.
Learning by induction is also one that is used frequently by humans . it is a powerful form of learning like analogical learning which also require s more inferring than the first two methods. This learning re quires the use of inductive inference, a form of invalid but useful inference. We use inductive learning of instances of examples of the concept. For example we learn the
concepts of color or sweet taste after experiencing the sensations associated with several examples of colored objects or sweet foods.
Deductive learning is accomplished through a sequence of deductive inference steps using known facts. From the known facts, new facts or relationships are logically derived. Deductive learning usually requires more inference than the other methods.